The application of soft systems thinking in SDG interaction studies: a comparison between SDG interactions at national and subnational levels in Colombia

Since the approval of the Agenda 2030, researchers and policy makers have pointed out the need to understand interactions among the Sustainable Development Goals (SDGs)-suggesting that progress or the lack of progress toward one goal will affect other goals through systemic interactions, producing synergies and trade-offs. However, most of the methods used to account for these interactions rely on hard systems thinking approaches, which are limited by the absence of needed data below national levels. Moreover, a general lack of data also constrains the scope of analysis to the 17 Goals, ignoring their 169 underlying targets. Given these challenges, we report on an experiment using an example of a soft systems thinking methodology: the SDG Synergies approach, which is based not only on available information but also on the elicitation of stakeholder and expert opinions. Thus, the approach allows for analysis of target-to-target interactions at subnational scales. The study, the first of its kind, assessed interactions at two scales: the national level in Colombia and the subnational level in the department of Antioquia. The results reveal profound differences between the two scales, suggesting that national-scale analysis of SDG interlinkages is not certain to capture local-level realities. The findings raise important issues for understanding and managing cross-scale interactions. Our work suggests that soft systems thinking is more appropriate for assessing SDG interactions because such an approach lends itself to conducting target-level analysis at various scales in the face of limited data availability.

Automated summary

Researchers have utilized a soft systems thinking approach, the SDG Synergies method, to assess interactions among Sustainable Development Goals at different scales, revealing significant differences between national and subnational levels. This highlights the limitations of hard systems thinking methods in the face of data constraints, suggesting that soft systems thinking is more suitable for cross-scale analysis.